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Quantile dispersion graphs to compare the efficiencies of cluster randomized designs

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  • S. Mukhopadhyay
  • S. W. Looney

Abstract

The purpose of this article is to compare efficiencies of several cluster randomized designs using the method of quantile dispersion graphs (QDGs). A cluster randomized design is considered whenever subjects are randomized at a group level but analyzed at the individual level. A prior knowledge of the correlation existing between subjects within the same cluster is necessary to design these cluster randomized trials. Using the QDG approach, we are able to compare several cluster randomized designs without requiring any information on the intracluster correlation. For a given design, several quantiles of the power function, which are directly related to the effect size, are obtained for several effect sizes. The quantiles depend on the intracluster correlation present in the model. The dispersion of these quantiles over the space of the unknown intracluster correlation is determined, and then depicted by the QDGs. Two applications of the proposed methodology are presented.

Suggested Citation

  • S. Mukhopadhyay & S. W. Looney, 2009. "Quantile dispersion graphs to compare the efficiencies of cluster randomized designs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1293-1305.
    • Handle: RePEc:taf:japsta:v:36:y:2009:i:11:p:1293-1305
    DOI: 10.1080/02664760902914508
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    References listed on IDEAS

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    1. Khuri, AndreI. & Lee, Juneyoung, 1998. "A graphical approach for evaluating and comparing designs for nonlinear models," Computational Statistics & Data Analysis, Elsevier, vol. 27(4), pages 433-443, June.
    2. Simpson, J.M. & Klar, N. & Donner, A., 1995. "Accounting for cluster randomization: A review of primary prevention trials, 1990 through 1993," American Journal of Public Health, American Public Health Association, vol. 85(10), pages 1378-1383.
    3. Byoung Cheol Jung & André Khuri & Juneyoung Lee, 2008. "Comparison of designs for the three-fold nested random model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(6), pages 701-715.
    4. Robinson, Kevin S. & Khuri, Andre I., 2003. "Quantile dispersion graphs for evaluating and comparing designs for logistic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 43(1), pages 47-62, May.
    5. A. I. Khuri, 1997. "Quantile dispersion graphs for analysis of variance estimates of variance components," Journal of Applied Statistics, Taylor & Francis Journals, vol. 24(6), pages 711-722.
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    Cited by:

    1. S.P. Singh & S. Mukhopadhyay & A. Roy, 2015. "Comparison of three-level cluster randomized trials using quantile dispersion graphs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(8), pages 1792-1812, August.

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